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st: RE: RE: gamma family - alpha and theta parameters - xtgee

 From "Guillermo A. Sandoval (UofT)" To "statalist@hsphsun2.harvard.edu" Subject st: RE: RE: gamma family - alpha and theta parameters - xtgee Date Wed, 29 May 2013 15:07:09 +0000

```Many thanks Alan. This is very useful. I did use gamma and log as the link function. I suppose what you found also applies. see my output below. It looks to me that a is 1 in my case.

GEE population-averaged model                   Number of obs      =       475
Group and time vars:            FacNo Year      Number of groups   =       123
Link:                                  log      Obs per group: min =         2
Family:                              gamma                     avg =       3.9
Correlation:                         AR(1)                     max =         4
Wald chi2(40)      =    612.84
Scale parameter:                  1.002232      Prob > chi2        =    0.0000

G

----
Guillermo A. Sandoval
PhD Candidate in Health Services Research

Institute of Health Policy, Management and Evaluation
University of Toronto

________________________________________
From: owner-statalist@hsphsun2.harvard.edu [owner-statalist@hsphsun2.harvard.edu] on behalf of Feiveson, Alan H. (JSC-SK311) [alan.h.feiveson@nasa.gov]
Sent: May 29, 2013 10:24 AM
To: statalist@hsphsun2.harvard.edu
Subject: st: RE: gamma family - alpha and theta parameters - xtgee

In partial response to Guillermo's question, I simulated some gamma data and tried - glm- to see what the "scale" parameter in Stata is an estimate of:

rgamma(a, b) Description:  returns gamma(a,b) random variates, where a is the gamma shape parameter and b is the scale parameter.

. gen y = rgamma(2,.01)

. summ y

Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
y |     10000    .0199965     .014057   .0003844   .1184808

Generalized linear models                          No. of obs      =     10000
Optimization     : ML                              Residual df     =      9999
Scale parameter =  .4941738
Deviance         =  5407.647596                    (1/df) Deviance =  .5408188
Pearson          =  4941.243902                    (1/df) Pearson  =  .4941738

Variance function: V(u) = u^2                      [Gamma]
Link function    : g(u) = u                        [Identity]

AIC             = -5.824201
Log likelihood   =  29122.00461                    BIC             = -86686.55

------------------------------------------------------------------------------
|                 OIM
y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons |   .0199965   .0001406   142.25   0.000     .0197209     .020272
------------------------------------------------------------------------------

Note that the "scale" parameter value of .4941738 is not 0.01 (the value of "b") but appears to be an estimate of 1/a = 0.5.

Note that this does not depend on the value of "b":

. replace y = rgamma(2,34567)

Generalized linear models                          No. of obs      =     10000
Optimization     : ML                              Residual df     =      9999
Scale parameter =  .4963884
Deviance         =  5469.254185                    (1/df) Deviance =  .5469801
Pearson          =  4963.387852                    (1/df) Pearson  =  .4963884

Variance function: V(u) = u^2                      [Gamma]
Link function    : g(u) = u                        [Identity]

AIC             =  24.32117
Log likelihood   = -121604.8625                    BIC             = -86624.94

------------------------------------------------------------------------------
|                 OIM
y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons |   70297.13   495.2803   141.93   0.000      69326.4    71267.86
------------------------------------------------------------------------------

However if I change the value of "a" (for any "b"):
. replace y  = rgamma(10,1232)

Generalized linear models                          No. of obs      =     10000
Optimization     : ML                              Residual df     =      9999
Scale parameter =  .1023587
Deviance         =  1038.561081                    (1/df) Deviance =  .1038665
Pearson          =  1023.484523                    (1/df) Pearson  =  .1023587

Variance function: V(u) = u^2                      [Gamma]
Link function    : g(u) = u                        [Identity]

AIC             =  20.85373
Log likelihood   = -104267.6349                    BIC             = -91055.63

------------------------------------------------------------------------------
|                 OIM
y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons |   12416.28   39.72747   312.54   0.000     12338.41    12494.14
------------------------------------------------------------------------------

The "scale" parameter estimate is now close to 0.1, which is 1/"a".

One more test:

. replace y = rgamma(2.5,.765)

Generalized linear models                          No. of obs      =     10000
Optimization     : ML                              Residual df     =      9999
Scale parameter =  .4056914
Deviance         =   4317.23078                    (1/df) Deviance =  .4317663
Pearson          =  4056.508667                    (1/df) Pearson  =  .4056914

Variance function: V(u) = u^2                      [Gamma]
Link function    : g(u) = u                        [Identity]

AIC             =  3.291668
Log likelihood   = -16457.33854                    BIC             = -87776.96

------------------------------------------------------------------------------
|                 OIM
y |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
_cons |   1.907386    .012149   157.00   0.000     1.883575    1.931198
------------------------------------------------------------------------------

Again, the "scale" parameter estimate is close to 1/"a" = 0.4.

Bottom line: With a gamma family, the "scale" parameter in -glm- (and also in -xtgee-) is an estimate of 1/"a", and does not appear to depend on the value of the "real" scale parameter "b".

Al Feiveson

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Guillermo A. Sandoval (UofT)
Sent: Tuesday, May 28, 2013 6:37 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: gamma family - alpha and theta parameters - xtgee

Dear
I am using xtgee with gamma distribution. I understand that for gamma distribution, one needs to define the parameters alpha and theta, which define the shape of the distribution function. Please see http://en.wikipedia.org/wiki/Gamma_distribution. My variable is continuous (mortality rates in hospitals) and distributes pretty much like the one in wekipedia that says k=1 and theta=2.

In xtgee, it looks like one cannot define these parameters. I guess my question is, is this necessary (if yes, how)? or xtgee  actually estimates them and I don't really need to worry about that?

Guillermo

----
Guillermo A. Sandoval
PhD Candidate in Health Services Research

Institute of Health Policy, Management and Evaluation University of Toronto

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